Overview

Dataset statistics

Number of variables22
Number of observations39118
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 MiB
Average record size in memory176.0 B

Variable types

NUM11
CAT10
BOOL1

Warnings

euribor3m is highly correlated with emp_var_rate and 1 other fieldsHigh correlation
emp_var_rate is highly correlated with euribor3m and 1 other fieldsHigh correlation
nr_employed is highly correlated with emp_var_rate and 1 other fieldsHigh correlation
df_index has unique values Unique
previous has 35058 (89.6%) zeros Zeros

Reproduction

Analysis started2021-03-16 21:37:09.352716
Analysis finished2021-03-16 21:38:05.672796
Duration56.32 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct39118
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20606.27517
Minimum0
Maximum41187
Zeros1
Zeros (%)< 0.1%
Memory size305.6 KiB
2021-03-16T22:38:05.940491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2061.85
Q110296.25
median20621.5
Q330912.75
95-th percentile39137.15
Maximum41187
Range41187
Interquartile range (IQR)20616.5

Descriptive statistics

Standard deviation11894.35685
Coefficient of variation (CV)0.5772201311
Kurtosis-1.200994653
Mean20606.27517
Median Absolute Deviation (MAD)10309.5
Skewness-0.001212727214
Sum806076272
Variance141475725
MonotocityStrictly increasing
2021-03-16T22:38:06.246838image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
279751< 0.1%
 
95501< 0.1%
 
156931< 0.1%
 
136441< 0.1%
 
34031< 0.1%
 
13541< 0.1%
 
74971< 0.1%
 
54481< 0.1%
 
259261< 0.1%
 
Other values (39108)39108> 99.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
31< 0.1%
 
51< 0.1%
 
61< 0.1%
 
ValueCountFrequency (%) 
411871< 0.1%
 
411861< 0.1%
 
411851< 0.1%
 
411841< 0.1%
 
411831< 0.1%
 

age
Real number (ℝ≥0)

Distinct74
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.72825809
Minimum17
Maximum95
Zeros0
Zeros (%)0.0%
Memory size305.6 KiB
2021-03-16T22:38:06.605269image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile57
Maximum95
Range78
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.791754256
Coefficient of variation (CV)0.2464682502
Kurtosis0.1940232706
Mean39.72825809
Median Absolute Deviation (MAD)7
Skewness0.6150391948
Sum1554090
Variance95.8784514
MonotocityNot monotonic
2021-03-16T22:38:06.965230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3118844.8%
 
3217874.6%
 
3317834.6%
 
3617314.4%
 
3517054.4%
 
3416794.3%
 
3016514.2%
 
3714113.6%
 
3914033.6%
 
2913853.5%
 
Other values (64)2269958.0%
 
ValueCountFrequency (%) 
171< 0.1%
 
1812< 0.1%
 
19280.1%
 
20490.1%
 
21830.2%
 
ValueCountFrequency (%) 
951< 0.1%
 
892< 0.1%
 
8816< 0.1%
 
871< 0.1%
 
863< 0.1%
 

job
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.6 KiB
admin.
9844 
blue-collar
9126 
technician
6463 
services
3886 
management
2790 
Other values (7)
7009 
ValueCountFrequency (%) 
admin.984425.2%
 
blue-collar912623.3%
 
technician646316.5%
 
services38869.9%
 
management27907.1%
 
entrepreneur14263.6%
 
self-employed13783.5%
 
retired13043.3%
 
housemaid9902.5%
 
unemployed9332.4%
 
Other values (2)9782.5%
 
2021-03-16T22:38:07.290418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-16T22:38:07.550679image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length10
Mean length9.006288665
Min length6

marital
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.6 KiB
married
23795 
single
10864 
divorced
4386 
unknown
 
73
ValueCountFrequency (%) 
married2379560.8%
 
single1086427.8%
 
divorced438611.2%
 
unknown730.2%
 
2021-03-16T22:38:07.843064image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-16T22:38:08.058123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:08.277341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length7
Mean length6.834398487
Min length6

education
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.6 KiB
university.degree
11425 
high.school
9081 
basic.9y
5908 
professional.course
4974 
basic.4y
3900 
Other values (3)
3830 
ValueCountFrequency (%) 
university.degree1142529.2%
 
high.school908123.2%
 
basic.9y590815.1%
 
professional.course497412.7%
 
basic.4y390010.0%
 
basic.6y22445.7%
 
unknown15694.0%
 
illiterate17< 0.1%
 
2021-03-16T22:38:08.575044image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-16T22:38:08.778505image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:09.064407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length11
Mean length12.68446751
Min length7

default
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.6 KiB
no
30585 
unknown
8530 
yes
 
3
ValueCountFrequency (%) 
no3058578.2%
 
unknown853021.8%
 
yes3< 0.1%
 
2021-03-16T22:38:09.325301image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-16T22:38:09.544529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:09.709703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length2
Mean length3.090367606
Min length2

housing
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.6 KiB
yes
20450 
no
17732 
unknown
 
936
ValueCountFrequency (%) 
yes2045052.3%
 
no1773245.3%
 
unknown9362.4%
 
2021-03-16T22:38:09.957750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-16T22:38:10.376409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:10.653080image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length2.642415256
Min length2

loan
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.6 KiB
no
32236 
yes
5946 
unknown
 
936
ValueCountFrequency (%) 
no3223682.4%
 
yes594615.2%
 
unknown9362.4%
 
2021-03-16T22:38:10.999122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-16T22:38:11.200711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:11.432001image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length2
Mean length2.271639654
Min length2

contact
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.6 KiB
cellular
24285 
telephone
14833 
ValueCountFrequency (%) 
cellular2428562.1%
 
telephone1483337.9%
 
2021-03-16T22:38:11.713385image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-16T22:38:11.856273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:12.028033image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length8
Mean length8.379186052
Min length8

month
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.6 KiB
may
13652 
jul
7054 
aug
5911 
jun
5206 
nov
3732 
Other values (5)
3563 
ValueCountFrequency (%) 
may1365234.9%
 
jul705418.0%
 
aug591115.1%
 
jun520613.3%
 
nov37329.5%
 
apr25436.5%
 
mar4401.1%
 
sep2700.7%
 
oct2020.5%
 
dec1080.3%
 
2021-03-16T22:38:12.271960image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-16T22:38:12.532280image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:12.835951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

day_of_week
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.6 KiB
thu
8183 
mon
8094 
wed
7739 
tue
7657 
fri
7445 
ValueCountFrequency (%) 
thu818320.9%
 
mon809420.7%
 
wed773919.8%
 
tue765719.6%
 
fri744519.0%
 
2021-03-16T22:38:13.087183image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-16T22:38:13.289491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:13.497833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

duration
Real number (ℝ≥0)

Distinct1498
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253.0746715
Minimum0
Maximum4918
Zeros4
Zeros (%)< 0.1%
Memory size305.6 KiB
2021-03-16T22:38:13.770703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q1100
median177
Q3314
95-th percentile736
Maximum4918
Range4918
Interquartile range (IQR)214

Descriptive statistics

Standard deviation251.9674239
Coefficient of variation (CV)0.9956248186
Kurtosis18.77168096
Mean253.0746715
Median Absolute Deviation (MAD)92
Skewness3.151387047
Sum9899775
Variance63487.58271
MonotocityNot monotonic
2021-03-16T22:38:14.150031image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
851680.4%
 
901640.4%
 
1361610.4%
 
731590.4%
 
1241590.4%
 
1111570.4%
 
871570.4%
 
721550.4%
 
1091530.4%
 
1061530.4%
 
Other values (1488)3753295.9%
 
ValueCountFrequency (%) 
04< 0.1%
 
11< 0.1%
 
21< 0.1%
 
33< 0.1%
 
411< 0.1%
 
ValueCountFrequency (%) 
49181< 0.1%
 
36431< 0.1%
 
36311< 0.1%
 
34221< 0.1%
 
33661< 0.1%
 

campaign
Real number (ℝ≥0)

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.595505905
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Memory size305.6 KiB
2021-03-16T22:38:14.477986image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.813804537
Coefficient of variation (CV)1.08410639
Kurtosis36.22037386
Mean2.595505905
Median Absolute Deviation (MAD)1
Skewness4.7284533
Sum101531
Variance7.917495972
MonotocityNot monotonic
2021-03-16T22:38:14.738039image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%) 
11659142.4%
 
21004125.7%
 
3510313.0%
 
425526.5%
 
515393.9%
 
69392.4%
 
76051.5%
 
83881.0%
 
92790.7%
 
102210.6%
 
Other values (32)8602.2%
 
ValueCountFrequency (%) 
11659142.4%
 
21004125.7%
 
3510313.0%
 
425526.5%
 
515393.9%
 
ValueCountFrequency (%) 
561< 0.1%
 
432< 0.1%
 
422< 0.1%
 
411< 0.1%
 
402< 0.1%
 

pdays
Real number (ℝ≥0)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean991.0291426
Minimum0
Maximum999
Zeros7
Zeros (%)< 0.1%
Memory size305.6 KiB
2021-03-16T22:38:14.994891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation88.61100315
Coefficient of variation (CV)0.08941311546
Kurtosis119.6114372
Mean991.0291426
Median Absolute Deviation (MAD)0
Skewness-11.02738556
Sum38767078
Variance7851.90988
MonotocityNot monotonic
2021-03-16T22:38:15.229524image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
9993880499.2%
 
3860.2%
 
6430.1%
 
2350.1%
 
12300.1%
 
10230.1%
 
1116< 0.1%
 
415< 0.1%
 
514< 0.1%
 
913< 0.1%
 
Other values (9)390.1%
 
ValueCountFrequency (%) 
07< 0.1%
 
19< 0.1%
 
2350.1%
 
3860.2%
 
415< 0.1%
 
ValueCountFrequency (%) 
9993880499.2%
 
222< 0.1%
 
162< 0.1%
 
152< 0.1%
 
144< 0.1%
 

previous
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1114832047
Minimum0
Maximum6
Zeros35058
Zeros (%)89.6%
Memory size305.6 KiB
2021-03-16T22:38:15.443419image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3419828429
Coefficient of variation (CV)3.06757277
Kurtosis15.42839576
Mean0.1114832047
Median Absolute Deviation (MAD)0
Skewness3.393408484
Sum4361
Variance0.1169522649
MonotocityNot monotonic
2021-03-16T22:38:15.672183image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
03505889.6%
 
137919.7%
 
22490.6%
 
312< 0.1%
 
45< 0.1%
 
52< 0.1%
 
61< 0.1%
 
ValueCountFrequency (%) 
03505889.6%
 
137919.7%
 
22490.6%
 
312< 0.1%
 
45< 0.1%
 
ValueCountFrequency (%) 
61< 0.1%
 
52< 0.1%
 
45< 0.1%
 
312< 0.1%
 
22490.6%
 

poutcome
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.6 KiB
nonexistent
35058 
failure
3753 
success
 
307
ValueCountFrequency (%) 
nonexistent3505889.6%
 
failure37539.6%
 
success3070.8%
 
2021-03-16T22:38:15.912919image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-16T22:38:16.106635image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:16.330224image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length10.58484585
Min length7

emp_var_rate
Real number (ℝ)

HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2121044021
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Memory size305.6 KiB
2021-03-16T22:38:16.549119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-1.8
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.486356203
Coefficient of variation (CV)7.007663155
Kurtosis-0.976048548
Mean0.2121044021
Median Absolute Deviation (MAD)0.3
Skewness-0.8142475073
Sum8297.1
Variance2.209254763
MonotocityNot monotonic
2021-03-16T22:38:16.778857image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.41622341.5%
 
-1.8887322.7%
 
1.1776219.8%
 
-0.136659.4%
 
-2.914473.7%
 
-1.75011.3%
 
-3.42950.8%
 
-1.12440.6%
 
-3980.3%
 
-0.210< 0.1%
 
ValueCountFrequency (%) 
-3.42950.8%
 
-3980.3%
 
-2.914473.7%
 
-1.8887322.7%
 
-1.75011.3%
 
ValueCountFrequency (%) 
1.41622341.5%
 
1.1776219.8%
 
-0.136659.4%
 
-0.210< 0.1%
 
-1.12440.6%
 

cons_price_idx
Real number (ℝ≥0)

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.59324845
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Memory size305.6 KiB
2021-03-16T22:38:16.996866image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.893
Q193.075
median93.897
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.553068145
Coefficient of variation (CV)0.00590927395
Kurtosis-0.8647694777
Mean93.59324845
Median Absolute Deviation (MAD)0.453
Skewness-0.1996067867
Sum3661180.693
Variance0.305884373
MonotocityNot monotonic
2021-03-16T22:38:17.283565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
93.994776219.8%
 
93.918667817.1%
 
92.893577114.8%
 
93.444517113.2%
 
94.465437411.2%
 
93.235989.2%
 
93.07524426.2%
 
92.9636911.8%
 
92.2015931.5%
 
92.8432750.7%
 
Other values (16)17634.5%
 
ValueCountFrequency (%) 
92.2015931.5%
 
92.3791030.3%
 
92.431740.2%
 
92.4691630.4%
 
92.6491180.3%
 
ValueCountFrequency (%) 
94.76716< 0.1%
 
94.601610.2%
 
94.465437411.2%
 
94.2152130.5%
 
94.1991670.4%
 

cons_conf_idx
Real number (ℝ)

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.77193875
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Memory size305.6 KiB
2021-03-16T22:38:17.537808image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-36.1
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.269385747
Coefficient of variation (CV)-0.1047138272
Kurtosis-0.8181031648
Mean-40.77193875
Median Absolute Deviation (MAD)4.4
Skewness0.1442791336
Sum-1594916.7
Variance18.22765466
MonotocityNot monotonic
2021-03-16T22:38:17.897575image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
-36.4776219.8%
 
-42.7667817.1%
 
-46.2577114.8%
 
-36.1517113.2%
 
-41.8437411.2%
 
-4235989.2%
 
-47.124426.2%
 
-40.86911.8%
 
-31.45931.5%
 
-502750.7%
 
Other values (16)17634.5%
 
ValueCountFrequency (%) 
-50.816< 0.1%
 
-502750.7%
 
-49.5610.2%
 
-47.124426.2%
 
-46.2577114.8%
 
ValueCountFrequency (%) 
-26.9740.2%
 
-29.81030.3%
 
-30.11180.3%
 
-31.45931.5%
 
-33980.3%
 

euribor3m
Real number (ℝ≥0)

HIGH CORRELATION

Distinct311
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.766859553
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Memory size305.6 KiB
2021-03-16T22:38:18.175738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.899
Q11.405
median4.857
Q34.962
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.557

Descriptive statistics

Standard deviation1.653674566
Coefficient of variation (CV)0.4390061648
Kurtosis-1.147991651
Mean3.766859553
Median Absolute Deviation (MAD)0.107
Skewness-0.870283185
Sum147352.012
Variance2.73463957
MonotocityNot monotonic
2021-03-16T22:38:18.482565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4.85728687.3%
 
4.96226106.7%
 
4.96324856.4%
 
4.96119024.9%
 
4.85612103.1%
 
4.96411753.0%
 
1.40511663.0%
 
4.96510692.7%
 
4.86410442.7%
 
4.9610132.6%
 
Other values (301)2257657.7%
 
ValueCountFrequency (%) 
0.6346< 0.1%
 
0.635290.1%
 
0.63612< 0.1%
 
0.6373< 0.1%
 
0.6383< 0.1%
 
ValueCountFrequency (%) 
5.0459< 0.1%
 
57< 0.1%
 
4.971720.4%
 
4.9689912.5%
 
4.9676431.6%
 

nr_employed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5175.150199
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Memory size305.6 KiB
2021-03-16T22:38:18.772607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5076.2
Q15099.1
median5195.8
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation64.01678691
Coefficient of variation (CV)0.01237003458
Kurtosis0.2550537963
Mean5175.150199
Median Absolute Deviation (MAD)32.3
Skewness-1.105565903
Sum202441525.5
Variance4098.149007
MonotocityNot monotonic
2021-03-16T22:38:19.070773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
5228.11622341.5%
 
5099.1848821.7%
 
5191776219.8%
 
5195.836659.4%
 
5076.214473.7%
 
4991.65011.3%
 
5008.73851.0%
 
5017.52950.8%
 
4963.62440.6%
 
5023.5980.3%
 
ValueCountFrequency (%) 
4963.62440.6%
 
4991.65011.3%
 
5008.73851.0%
 
5017.52950.8%
 
5023.5980.3%
 
ValueCountFrequency (%) 
5228.11622341.5%
 
5195.836659.4%
 
5191776219.8%
 
5176.310< 0.1%
 
5099.1848821.7%
 

y
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.6 KiB
0
35627 
1
 
3491
ValueCountFrequency (%) 
03562791.1%
 
134918.9%
 
2021-03-16T22:38:19.186540image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Interactions

2021-03-16T22:37:27.089236image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:27.499162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:27.912042image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:28.332639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:28.710913image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:29.022412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:29.312559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:29.568792image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:29.829112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:30.088370image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:30.354361image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:30.662000image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:30.935148image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:31.248190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:31.628159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:31.911912image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:32.227960image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:32.586005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:32.917510image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:33.312237image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:33.618536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:33.883159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:34.157273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:34.415519image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:34.752481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:35.084475image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:35.332890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:35.588945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:35.846267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:36.073640image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:36.374307image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:36.639721image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:36.920772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:37.176453image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:37.459315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:37.746481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:38.026433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:38.279035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:38.541876image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:38.801863image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:39.104632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:39.393336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:39.666470image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:39.927809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:40.211482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:40.416650image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:40.713787image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:40.970431image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:41.238034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:41.481592image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:41.761974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:42.043905image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:42.357420image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:42.625302image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:43.090224image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:43.365433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:43.655975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:43.959162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:44.258176image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:44.520240image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:44.798153image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:45.055866image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:45.345893image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:45.611704image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:45.899243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:46.159044image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:46.427156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:46.718809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:47.032433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:47.319063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:47.607694image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:47.873273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:48.164837image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:48.561603image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:48.925525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:49.260697image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:49.576052image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:49.869410image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:50.171509image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:50.473989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:50.735535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:51.037425image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:51.398741image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:51.747331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:52.043287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:52.320282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:52.641660image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:52.910150image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:53.210453image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:53.459977image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:53.717175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:53.987170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:54.325118image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:54.591207image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:55.111376image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:55.518242image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:55.899378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:56.451530image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:56.728266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:57.037840image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:57.281076image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:57.677770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:57.985513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:58.312034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:58.623984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:59.003156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:59.341419image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:59.597949image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:37:59.879094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:00.176539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:00.427991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:00.774167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:01.034196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:01.299472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:01.546109image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:01.795468image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:02.048511image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:02.292740image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:02.567492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:02.826352image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:03.064347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-03-16T22:38:19.344089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-16T22:38:19.758173image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-16T22:38:20.203601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-16T22:38:20.717446image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-16T22:38:21.259090image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-16T22:38:03.759634image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-16T22:38:05.032052image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

df_indexagejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp_var_ratecons_price_idxcons_conf_idxeuribor3mnr_employedy
0044blue-collarmarriedbasic.4yunknownyesnocellularaugthu21019990nonexistent1.493.444-36.14.9635228.10
1153technicianmarriedunknownnononocellularnovfri13819990nonexistent-0.193.200-42.04.0215195.80
2339servicesmarriedhigh.schoolnononocellularaprfri18529990nonexistent-1.893.075-47.11.4055099.10
3530managementdivorcedbasic.4ynoyesnocellularjultue6889990nonexistent1.493.918-42.74.9615228.10
4637blue-collarmarriedbasic.4ynoyesnocellularmaythu20419990nonexistent-1.892.893-46.21.3275099.10
5739blue-collardivorcedbasic.9ynoyesnocellularmayfri19119990nonexistent-1.892.893-46.21.3135099.10
6836admin.marrieduniversity.degreenononocellularjunmon174131success-2.992.963-40.81.2665076.21
7927blue-collarsinglebasic.4ynoyesnocellularaprthu19129991failure-1.893.075-47.11.4105099.10
81034housemaidsingleuniversity.degreenononotelephonemayfri6229990nonexistent1.193.994-36.44.8645191.00
91141managementmarrieduniversity.degreenoyesnocellularaugthu78919990nonexistent1.493.444-36.14.9645228.10

Last rows

df_indexagejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp_var_ratecons_price_idxcons_conf_idxeuribor3mnr_employedy
391084117752self-employedsingleuniversity.degreeunknownyesnotelephonejunfri7319990nonexistent1.494.465-41.84.9675228.10
391094117835technicianmarriedhigh.schoolnononotelephoneaugfri24319990nonexistent1.493.444-36.14.9665228.11
391104117929techniciansinglebasic.9ynoyesnocellularmaymon21419990nonexistent-1.892.893-46.21.2995099.10
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